Environment-friendly PCN derivatives design and environmental behavior simulation based on a multi-activity 3D-QSAR...

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Toxicology

Background:

• Polychlorinated naphthalenes (PCNs) are persistent organic pollutants with significant environmental and health concerns.
• Existing quantitative structure-activity relationship (QSAR) models often focus on single pollutant activities, limiting comprehensive molecular design.
• Developing models that account for multiple pollutant activities is crucial for designing safer chemical alternatives.

Purpose of the Study:

• To establish a multi-activity three-dimensional quantitative structure-activity relationship (3D-QSAR) model for polychlorinated naphthalenes (PCNs).
• To design novel PCN derivatives with reduced environmental impact and improved degradation properties.
• To validate the model's predictions through environmental behavior simulations and molecular docking.

Main Methods:

• Established a 3D-QSAR model using a comprehensive evaluation index (CEI) incorporating biological toxicity, bioconcentration, long-distance migration, and biodegradation.
• Designed 64 CN-70 derivatives with lower CEI values and selected three for further evaluation.
• Employed molecular dynamics simulation and Taguchi experimental design for environmental behavior simulation and degradation analysis.

Main Results:

• Two designed CN-70 derivatives exhibited enhanced degradation (11.57%-13.57%) compared to the parent molecule in real-world simulations.
• Biodegradability predictions from molecular docking aligned with simulation results, showing increased degradation (14.94%-22.49%).
• The multi-activity 3D-QSAR model successfully guided the design of derivatives with improved environmental profiles.

Conclusions:

• The developed multi-activity 3D-QSAR model offers a superior approach for designing environmentally benign chemical compounds compared to single-activity models.
• The study demonstrates the potential of computational modeling in predicting and mitigating the environmental risks associated with persistent organic pollutants.
• The identified CN-70 derivatives represent promising candidates for reduced environmental persistence and improved degradability.